Most AI never ships.

And almost never because of the technology. The models work. The gap is everything around them: the spec, the evals, and the judgment about what to ship and what to leave alone. That gap is where I work, building AI that ships and takes cost out of real work, customer facing and internal.

Spec-driven, not vibe coding.

I write the spec, orchestrate the build through Claude Code, design the evals that prove it works, and own the outcome end to end. The result is production software, not a prototype that demos well and breaks on contact with reality.

The harder part is judgment: knowing where it is safe to move fast and iterate, where it has to be right before anything ships, and when to bring in a dedicated technical owner. That judgment is what keeps a project out of the majority that never deliver.

A new kind of builder.

I ship production software without hand writing the code, by combining systems thinking, AI orchestration, and relentless evaluation. It is where the work is heading, and it is how everything here was built.

Before this was a category, I trained the data behind frontier AI models. I mentored prompt engineering at Pareto.ai and ranked in the top 0.1 percent across more than 2,000 contributors. That is the lens I bring to building with these systems: I know how they are made, so I know where they break.

Open to the right hard problem.

I am looking to bring this operating capability inside one team solving frontier problems. Open to AI deployment, implementation, and solutions advisory at AI-native companies. Regulated or high-stakes environments welcome. Remote primary, Tucson hybrid is fine.

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